With quanta-counting x-ray detectors, signal processing channels are often used, which determine the counting rate with different energy thresholds. As a result, a measurement of the x-ray signal is not only described by an individual value per channel, but instead by a value for each energy threshold. With similar spatial resolution (pixel dimension) and with N energy thresholds, this usually results in N-times the data quantity. The data quantity per energy threshold barely changes since the dynamic range remains approximately the same in each instance.
In x-ray applications with a high photon flow, in particular in the field of computed tomography (CT), there is also an increasing problem of the counting rate per pixel having to be kept as small as possible in order to avoid overlaying the individual pulses (“pulse pile-up”) and thus reducing the count efficiency. The surface of the pixel is therefore often segmented into M sub detector pixels so that the flow per sub detector pixel reduces to 1/M. However this results in a further increase in the data volume approximately by the factor M. Overall, with the same spatial resolution and same surface, the (raw) data volume therefore increases approximately by the factor M*N compared with an integrating detector in the CT.
In more precise terms, this means: since the surface of the pixel reduces, the dynamic range required per channel is also smaller by the factor 1/M, so that compared with the non-segmented pixel, the required data width per channel can be reduced by log 2(M) bits. If a pixel is subdivided, subsequently referred to as “macropixel”, into 4×4 sub detector pixels for instance, each sub detector pixel sees approximately one sixteenth of the flow so that the data width of the measured value can be reduced by 4 bits compared with the non-segmented pixel. The dynamic region of a 16-times segmented pixel with 16 bit per sub detector pixel therefore corresponds approximately to that of a non-segmented pixel with 20 bits. In the concrete case with M=16, N=4 and by taking the reduction from 20 bits to 16 bits into account, a higher raw data rate of a counting detector by the factor 50 (˜16*4*(16/20)) is thus produced with four energy thresholds and 16 sub detector pixels to a macropixel compared with a currently integrating detector system. Since the data volume to be transmitted of conventional integrating detectors represents a challenge, a corresponding problem results on account of the data quantity of quanta-counting detectors which is higher by the factor 50.
In the field of conventional, integrating detector systems, various methods of reducing data are known. Such methods can basically also be transferred to counting detector systems and in this way generate a comparable, relative data reduction, but the problem of the approximately 50 times higher data rate with quanta-counting detectors nevertheless remains unresolved here.
It is known with counting detectors to reduce the data rate, by reducing the number of evaluated energy thresholds, thereby resulting in a proportional saving and can possibly be partially acceptable depending on each clinical scenario. Nevertheless, the advantages of the energy resolution of the quanta-counting detectors are as a result also at least partially forfeited again. Furthermore, a data reduction can already take place in the detector, by the values of the sub detector pixels present there being combined to form an overall value for the macropixel. In the extreme case, the data quantity can as a result be reduced to the quantity of a conventional system. The higher intrinsic local resolution and the spectral information of the different energy thresholds are however lost here. A data reduction through loss of information is therefore only generated by means of these measures.